Advertisement exchange using neuro-response data

ABSTRACT

Example methods, systems and machine readable instructions are disclosed for assessing advertising effectiveness based on neurological data. An example method includes determining advertisement slot characteristics for a plurality of advertisement slots. The example advertisement slot characteristics include subject resonance measured by determining a first event related potential from neuro-response data gathered from multiple regions of a brain of a subject, determining a second event related potential from the neuro-response data and calculating a differential measurement of the first event related potential and the second event related potential, the subject resonance based on the differential measurement. The example method also includes matching the plurality of advertisement slots with a plurality of advertisements based on the advertisement slot characteristics.

RELATED APPLICATIONS

This patent arises from a continuation of U.S. patent application Ser.No. 12/622,292, entitled “Advertisement Exchange Using Neuro-ResponseData,” which was filed on Nov. 19, 2009, and a continuation of U.S.patent application Ser. No. 12/622,312, entitled “MultimediaAdvertisement Exchange,” which was filed on Nov. 19, 2009, both of whichare incorporated herein by reference in their entireties.

FIELD OF THE DISCLOSURE

The present disclosure relates to an advertisement exchange usingneuro-response data.

BACKGROUND

Conventional systems for selection, purchase, exchange, and placement ofadvertisements such as commercials, print banner advertisements, radiocommercials, etc., are limited or non-existent. Some conventionalsystems allow selection of slots based on perceived value. Analysisconducted to place advertising may involve evaluation of demographicinformation and statistical data. However, conventional systems aresubject to semantic, syntactic, metaphorical, cultural, and interpretiveerrors.

Consequently, it is desirable to provide improved methods and apparatusfor selection, purchase, exchange, and placement of advertising usingneuro-response data.

BRIEF DESCRIPTION OF THE DRAWINGS

The disclosure may best be understood by reference to the followingdescription taken in conjunction with the accompanying drawings, whichillustrate particular example embodiments.

FIG. 1A illustrates one example of a system for implementing anadvertisement exchange.

FIG. 1B illustrates an example of a system for obtaining neuro-responsedata.

FIG. 2 illustrates examples of stimulus attributes that can be includedin a repository.

FIG. 3 illustrates examples of data models that can be used with astimulus and response repository.

FIG. 4 illustrates one example of a query that can be used with theadvertisement exchange.

FIG. 5 illustrates one example of a report generated using theadvertisement exchange.

FIG. 6 illustrates one example of a technique for performing dataanalysis.

FIG. 7 illustrates one example of technique for advertisement exchangeimplementation.

FIG. 8 provides one example of a system that can be used to implementone or more mechanisms.

DETAILED DESCRIPTION

Reference will now be made in detail to some specific examples of thedisclosure including the best modes contemplated by the inventors forcarrying out the systems and methods of the disclosure. Examples ofthese specific embodiments are illustrated in the accompanying drawings.While the systems and methods of the disclosure are described inconjunction with these specific embodiments, it will be understood thatit is not intended to limit the disclosure to the described embodiments.On the contrary, it is intended to cover alternatives, modifications,and equivalents as may be included within the spirit and scope of thedisclosure as defined by the appended claims.

For example, the techniques and mechanisms of the present disclosurewill be described in the context of an advertisement exchange usingneuro-response data. However, it should be noted that some of thetechniques and mechanisms for implementing an advertisement exchange maybe applied using other data such as survey data and demographic dataeven without the use of neuro-response data. In the followingdescription, numerous specific details are set forth in order to providea thorough understanding of the present disclosure. Particular exampleembodiments of the present disclosure may be implemented without some orall of these specific details. In other instances, well known processoperations have not been described in detail in order not tounnecessarily obscure the present disclosure.

Various techniques and mechanisms of the present disclosure willsometimes be described in singular form for clarity. However, it shouldbe noted that some embodiments include multiple iterations of atechnique or multiple instantiations of a mechanism unless notedotherwise. For example, a system uses a processor in a variety ofcontexts. However, it will be appreciated that a system can use multipleprocessors while remaining within the scope of the present disclosureunless otherwise noted. Furthermore, the techniques and mechanisms ofthe present disclosure will sometimes describe a connection between twoentities. It should be noted that a connection between two entities doesnot necessarily mean a direct, unimpeded connection, as a variety ofother entities may reside between the two entities. For example, aprocessor may be connected to memory, but it will be appreciated that avariety of bridges and controllers may reside between the processor andmemory. Consequently, a connection does not necessarily mean a direct,unimpeded connection unless otherwise noted.

Overview

An advertisement exchange determines characteristics associated withadvertisement slots such as slots in a commercial pod, locations on aprinted page, banners in a video, billboards, etc. Characteristics mayinclude demographic information, advertisement type, and neuro-responsecharacteristics such as priming, attention, engagement, and retention.Advertisement slots are matched with advertisements and may be selected,purchased, exchanged, and analyzed by advertisers, individuals,corporations, and firms. In some examples, bids and offers are made foradvertisement slots based on advertisement slot characteristics.Advertisement slot characteristics may be changed in real-time asplacement of advertisements in surrounding slots changes thecharacteristics of a particular slot.

Example Embodiments

Conventional mechanisms for managing advertisement slots includingselecting, purchasing, exchanging, analyzing, and selling advertisementslots are limited. One problem with conventional mechanisms for managingadvertisement slots and advertising is that they do not allow efficientselection and purchase of advertisement slots based on characteristicsof various advertisement slots. For example, an insurance company may beable to select and buy advertisement slots based on programmingdemographic, but the insurance company does not have efficient access toinformation such as priming and retention characteristics for insurancecompany advertisements at particular advertisement slots. The insurancecompany also may not fully appreciate the advertisement slotcharacteristics of different media such as print, video, audio, banner,etc. Conventional mechanisms may allow for experience based selection ofadvertisement slots for various products, services, and offerings thatare attributable to the stimulus. However, they are also prone tosemantic, syntactic, metaphorical, cultural, and interpretive errorsthereby preventing the accurate and repeatable targeting of audiences.

Conventional systems do not use neuro-behavioral and neuro-physiologicalresponse blended manifestations in assessing the user response and donot elicit an individual customized neuro-physiological and/orneuro-behavioral response to the stimulus. Conventional systems alsofail to blend multiple datasets, and blended manifestations ofmulti-modal responses, across multiple datasets, individuals andmodalities, to reveal and validate the elicited measures of resonanceand priming to allow for intelligent selection of personalized content.

In these respects, the advertisement exchange according to the presentdisclosure substantially departs from the conventional concepts anddesigns of the prior art. According to various embodiments, it isrecognized that a subject commercial or advertisement for particularproducts, services, and offerings may be particularly effective when auser is primed for the particular products, services, and offerings byother commercials and advertisements in close proximity to the subjectcommercial or advertisements. For example, an advertisement for cleaningsupplies may be particularly effective for viewers who have viewed anadvertisement on antibacterial soap, or an advertisement for a sportscar may be particularly effective for viewers who have recently viewed acommercial for an auto racing program in the same commercial pod. Instill other examples, an audio advertisement for packaged salads may bemore effective after listening to an audio advertisement for a weightloss program in the same audio advertisement cluster.

It is also recognized that user attention, engagement, and retentionlevels at various points in a program or at various slots in acommercial pod may vary. The techniques and mechanisms of the presentdisclosure allow advertisement slot purchasers to account for thesedifferences during advertisement slot purchases. For example, it may bedetermined that the first slot during the first commercial break of aparticular program has the highest attention and engagement levels butmedium retention levels based on neuro-response data. Advertisement slotpurchasers may bid for the particular slot based on increasedinformation about advertisement slot characteristics and typical subjectneuro-response levels during the particular slot. Differentadvertisement slots in a commercial pod for the same program may varywidely in effectiveness levels, yet many advertisement slots are soldbased on flat rate or tiered type pricing structures.

The techniques and mechanisms of the present disclosure provide moreindividualized and/or auction based pricing for particular slots. Inparticular embodiments, purchasers are provided with cost per retentiongain or cost per engagement level scores for various slots in differentmedia.

Consequently, the techniques and mechanisms of the present disclosuredetermine characteristics of advertisement slots. In particularembodiments, characteristics of advertisements are also determined.Neuro-response characteristics such as priming, attention, engagement,and retention along with demographic data can be used to matchadvertisement slots with advertisement purchasers and advertisements. Insome examples, advertisement slots and advertisements are labeled andtagged to allow for improved selection and arrangement.

Advertisers can assess the value of particular slots such as a slot in acommercial pod, a line of text on an advertisement page, a location on astore shelf, etc., based on characteristics of the slots and ability toaccess to preferred audiences.

According to various embodiments, the techniques and mechanisms of thepresent disclosure may use a variety of mechanisms such as survey basedresponses, statistical data, and/or neuro-response measurements such ascentral nervous system, autonomic nervous system, and effectormeasurements to improve advertisement slot management. Some examples ofcentral nervous system measurement mechanisms include FunctionalMagnetic Resonance Imaging (fMRI) and Electroencephalography (EEG). fMRImeasures blood oxygenation in the brain that correlates with increasedneural activity. However, current implementations of fMRI have poortemporal resolution of few seconds. EEG measures electrical activityassociated with post synaptic currents occurring in the millisecondsrange. Subcranial EEG can measure electrical activity with the mostaccuracy, as the bone and dermal layers weaken transmission of a widerange of frequencies. Nonetheless, surface EEG provides a wealth ofelectrophysiological information if analyzed properly. Even portable EEGwith dry electrodes provides a large amount of neuro-responseinformation.

Autonomic nervous system measurement mechanisms include FunctionalMagnetic Resonance Imaging (FMRI), Electrocardiograms (EKG), pupillarydilation, etc. Effector measurement mechanisms includeElectrooculography (EOG), eye tracking, facial emotion encoding,reaction time etc.

According to various embodiments, the techniques and mechanisms of thepresent disclosure intelligently blend multiple modes and manifestationsof precognitive neural signatures with cognitive neural signatures andpost cognitive neurophysiological manifestations to more accuratelyperform advertisement slot management. In some examples, autonomicnervous system measures are themselves used to validate central nervoussystem measures. Effector and behavior responses are blended andcombined with other measures. According to various embodiments, centralnervous system, autonomic nervous system, and effector systemmeasurements are aggregated into a measurement that allows advertisementslot management and an advertisement exchange.

In particular embodiments, subjects are exposed to stimulus material anddata such as central nervous system, autonomic nervous system, andeffector data is collected during exposure. According to variousembodiments, data is collected in order to determine a resonance measurethat aggregates multiple component measures that assess resonance data.In particular embodiments, specific event related potential (ERP)analyses and/or event related power spectral perturbations (ERPSPs) areevaluated for different regions of the brain both before a subject isexposed to stimulus and each time after the subject is exposed tostimulus.

According to various embodiments, pre-stimulus and post-stimulusdifferential as well as target and distracter differential measurementsof ERP time domain components at multiple regions of the brain aredetermined (DERP). Event related time-frequency analysis of thedifferential response to assess the attention, emotion and memoryretention (DERPSPs) across multiple frequency bands including but notlimited to theta, alpha, beta, gamma and high gamma is performed. Inparticular embodiments, single trial and/or averaged DERP and/or DERPSPscan be used to enhance the resonance measure and determine priminglevels for various products and services.

According to various embodiments, enhanced neuro-response data isgenerated using a data analyzer that performs both intra-modalitymeasurement enhancements and cross-modality measurement enhancements.According to various embodiments, brain activity is measured not just todetermine the regions of activity, but to determine interactions andtypes of interactions between various regions. The techniques andmechanisms of the present disclosure recognize that interactions betweenneural regions support orchestrated and organized behavior. Attention,emotion, memory, and other abilities are not merely based on one part ofthe brain but instead rely on network interactions between brainregions.

The techniques and mechanisms of the present disclosure furtherrecognize that different frequency bands used for multi-regionalcommunication can be indicative of the effectiveness of stimuli. Inparticular embodiments, evaluations are calibrated to each subject andsynchronized across subjects. In particular embodiments, templates arecreated for subjects to create a baseline for measuring pre and poststimulus differentials. According to various embodiments, stimulusgenerators are intelligent and adaptively modify specific parameterssuch as exposure length and duration for each subject being analyzed.

A variety of modalities can be used including EEG, FMRI, EKG, pupillarydilation, EOG, eye tracking, facial emotion encoding, reaction time,etc. Individual modalities such as EEG are enhanced by intelligentlyrecognizing neural region communication pathways. Cross modalityanalysis is enhanced using a synthesis and analytical blending ofcentral nervous system, autonomic nervous system, and effectorsignatures. Synthesis and analysis by mechanisms such as time and phaseshifting, correlating, and validating intra-modal determinations allowgeneration of a composite output characterizing the significance ofvarious data responses to effectively characterize advertisement slotsfor an advertisement exchange.

FIG. 1A illustrates one example of an advertisement exchange. Anadvertisement exchange 112 receives available advertisements slots fromcontent provider 132, content provider 134, content aggregator 136, andadvertisement broker 138. Content providers may include media companies,cable companies, service providers, portals, publishers, etc. Contentaggregators 136 and advertisement brokers 138 may include companies thatsell advertising for multiple content providers. Content providers sendnotifications and/or listings of available advertisements slots.According to various embodiments, advertisement slots include acommercial in a commercial pod, space on a printed page, a wall in ascene of a video game, a billboard, text on a store shelf, etc. Contentproviders may also provide some characteristics of the advertise slotfor an advertisement slot characteristics database 126 associated withan advertisement exchange 112. Some characteristics may includedimensions of the slot, media type, duration of placement, cost,distribution, target demographic, associated programming, timeslot, etc.

According to various embodiments, an advertisement slot neuro-responsedatabase 128 is also associated with the advertisement exchange 112. Theadvertisement slot neuro-response database 128 may be integrated withthe advertisement slot characteristics database 126 or maintainedseparately. The advertisement slot neuro-response database 128 includescharacteristics such as attention, priming, retention, and engagementlevels for a particular advertisement slot. For example, priming levelsfor cleanser commercials during a documentary about infections may behigh. Retention levels for an advertisement slot during a particularaction sequence may be high. Neuro-response metrics are determined forvarious advertisement slots in commercial pods, printed banners,billboards, etc. using neuro-response data obtained from subjectsexposed to stimulus material. The neuro-response database 128 providesadvertisement slot purchasers with additional insight useful inassessing the value of particular advertisement slots.

In particular embodiments, an advertisement characteristics database 106is also associated with an advertisement exchange 112. The advertisementcharacteristics database 106 may be preloaded with advertisements thatadvertisers 102 and corporations/firms 104 provide to the advertisementexchange 112. The advertisement exchange may place advertisements insuitable slots based on characteristics of the advertisement. Accordingto various embodiments, the advertisement characteristics database 106may indicate that a particular commercial could best be placed in a slotwith a high priming metric for food. Advertisers may provideadvertisements to an advertisement exchange 112 to automatically placethe advertisements in advertisement slots that meet advertising criteriasuch as target audience exposure levels and retention metrics in a costeffective manner. In other examples, advertisers 102, corporations andfirms 104 may bid for particular advertisement slots or particular typesof advertisements slots. In particular embodiments, commercials thatreach a certain number of people in a particular demographic whilehaving specified priming and retention levels are provided for auction.Neuro-response characteristics such as priming, attention, engagement,and retention levels for particular categories, demographics, and typescan be obtained using a neuro-response data collection system.

It should be noted that although the advertisement exchange may useneuro-response data to enhance advertisement as well as advertisementslot evaluation, some advertisement exchanges can be implemented withoutthe use of neuro-response data.

FIG. 1B illustrates one example of a neuro-response data collectionsystem for determining advertisement slot and/or advertisementcharacteristics in an advertisement exchange. The system includes astimulus presentation device 101. According to various embodiments, thestimulus presentation device 101 is merely a display, monitor, screen,etc., that displays stimulus material to a user. The stimulus materialmay be a media clip, a commercial, pages of text, a brand image, aperformance, a magazine advertisement, a movie, an audio presentation,an advertisement, a banner ad, commercial, and may even involveparticular tastes, smells, textures and/or sounds. The stimuli caninvolve a variety of senses and occur with or without human supervision.Continuous and discrete modes are supported. According to variousembodiments, the stimulus presentation device 101 also has protocolgeneration capability to allow intelligent customization of stimuliprovided to multiple subjects in different markets.

According to various embodiments, stimulus presentation device 101 couldinclude devices such as televisions, cable consoles, computers andmonitors, projection systems, display devices, speakers, tactilesurfaces, etc., for presenting the stimuli including but not limited toadvertising and entertainment from different networks, local networks,cable channels, syndicated sources, websites, internet contentaggregators, portals, service providers, etc.

According to various embodiments, the subjects 103 are connected to datacollection devices 105. The data collection devices 105 may include avariety of neuro-response measurement mechanisms including neurologicaland neurophysiological measurements systems such as EEG, EOG, FMRI, EKG,pupillary dilation, eye tracking, facial emotion encoding, and reactiontime devices, etc. According to various embodiments, neuro-response dataincludes central nervous system, autonomic nervous system, and effectordata. In particular embodiments, the data collection devices 105 includeEEG 111, EOG 113, and FMRI 115. In some instances, only a single datacollection device is used. Data collection may proceed with or withouthuman supervision.

The data collection device 105 collects neuro-response data frommultiple sources. This includes a combination of devices such as centralnervous system sources (EEG), autonomic nervous system sources (FMRI,EKG, pupillary dilation), and effector sources (EOG, eye tracking,facial emotion encoding, reaction time). In particular embodiments, datacollected is digitally sampled and stored for later analysis. Inparticular embodiments, the data collected could be analyzed inreal-time. According to particular embodiments, the digital samplingrates are adaptively chosen based on the neurophysiological andneurological data being measured.

In one particular embodiment, the advertisement exchange includes EEG111 measurements made using scalp level electrodes, EOG 113 measurementsmade using shielded electrodes to track eye data, FMRI 115 measurementsperformed using a differential measurement system, a facial muscularmeasurement through shielded electrodes placed at specific locations onthe face, and a facial affect graphic and video analyzer adaptivelyderived for each individual.

In particular embodiments, the data collection devices are clocksynchronized with a stimulus presentation device 101. In particularembodiments, the data collection devices 105 also include a conditionevaluation subsystem that provides auto triggers, alerts and statusmonitoring and visualization components that continuously monitor thestatus of the subject, data being collected, and the data collectioninstruments. The condition evaluation subsystem may also present visualalerts and automatically trigger remedial actions. According to variousembodiments, the data collection devices include mechanisms for not onlymonitoring subject neuro-response to stimulus materials, but alsoinclude mechanisms for identifying and monitoring the stimulusmaterials. For example, data collection devices 105 may be synchronizedwith a set-top box to monitor channel changes. In other examples, datacollection devices 105 may be directionally synchronized to monitor whena subject is no longer paying attention to stimulus material. In stillother examples, the data collection devices 105 may receive and storestimulus material generally being viewed by the subject, whether thestimulus is a program, a commercial, printed material, or a sceneoutside a window. The data collected allows analysis of neuro-responseinformation and correlation of the information to actual stimulusmaterial and not mere subject distractions.

According to various embodiments, the advertisement exchange alsoincludes a data cleanser device 121. In particular embodiments, the datacleanser device 121 filters the collected data to remove noise,artifacts, and other irrelevant data using fixed and adaptive filtering,weighted averaging, advanced component extraction (like PCA, ICA),vector and component separation methods, etc. This device cleanses thedata by removing both exogenous noise (where the source is outside thephysiology of the subject, e.g. a phone ringing while a subject isviewing a video) and endogenous artifacts (where the source could beneurophysiological, e.g. muscle movements, eye blinks, etc.).

The artifact removal subsystem includes mechanisms to selectivelyisolate and review the response data and identify epochs with timedomain and/or frequency domain attributes that correspond to artifactssuch as line frequency, eye blinks, and muscle movements. The artifactremoval subsystem then cleanses the artifacts by either omitting theseepochs, or by replacing these epoch data with an estimate based on theother clean data (for example, an EEG nearest neighbor weightedaveraging approach).

According to various embodiments, the data cleanser device 121 isimplemented using hardware, firmware, and/or software. It should benoted that although a data cleanser device 121 is shown located after adata collection device 105 and before content characteristicsintegration 133, the data cleanser device 121 like other components mayhave a location and functionality that varies based on systemimplementation. For example, some systems may not use any automated datacleanser device whatsoever while in other systems, data cleanser devicesmay be integrated into individual data collection devices.

In particular embodiments, an optional survey and interview systemcollects and integrates user survey and interview responses to combinewith neuro-response data to more effectively select content fordelivery. According to various embodiments, the survey and interviewsystem obtains information about user characteristics such as age,gender, income level, location, interests, buying preferences, hobbies,etc. The survey and interview system can also be used to obtain userresponses about particular pieces of stimulus material.

According to various embodiments, the priming repository system 131associates meta-tags with various temporal and spatial locations inprogram content and provides these meta-tags to an advertisementcharacteristics database associated with an advertisement exchange. Insome examples, commercial or advertisement breaks are provided with aset of meta-tags that identify commercial or advertising content thatwould be most suitable for a particular advertisement slot. The slot maybe a particular position in a commercial pod or a particular location ona page.

Each slot may identify categories of products and services that areprimed at a particular point in a cluster. The content may also specifythe level of priming associated with each category of product orservice. For example, a first commercial may show an old house andbuildings. Meta-tags may be manually or automatically generated toindicate that commercials for home improvement products would besuitable for a particular advertisement slot or slots following thefirst commercial.

In some instances, meta-tags may include spatial and temporalinformation indicating where and when particular advertisements shouldbe placed. For example, a page that includes advertisements about petadoptions may indicate that a banner advertisement for pet care relatedproducts may be suitable. The advertisements may be separate from aprogram or integrated into a program. According to various embodiments,the priming repository system 131 also identifies scenes elicitingsignificant audience resonance to particular products and services aswell as the level and intensity of resonance. The information in thepriming repository system 131 may be manually or automatically generatedand may be associated with other characteristics such as retention,attention, and engagement characteristics. In some examples, the primingrepository system 131 has data generated by determining resonancecharacteristics for temporal and spatial locations in various programs,games, commercial pods, pages, etc.

The information from a priming, attention, engagement, and retentionrepository system 131 may be combined along with type, demographic,time, and modality information using a content characteristicsintegration system 133. According to various embodiments, the contentcharacteristics integration system weighs and combines components ofpriming, attention, engagement, retention, personalization,demographics, etc. to allow selection, purchase, and placement ofadvertising in effective advertisement slots. The material may bemarketing, entertainment, informational, etc.

In particular embodiments, neuro-response preferences are blended withconscious, indicated, and/or inferred user preferences to selectneurologically effective advertising for presentation to the user. Inone particular example, neuro-response data may indicate that beverageadvertisements would be suitable for a particular advertisement break.User preferences may indicate that a particular viewer prefers dietsodas. An advertisement for a low calorie beverage may be selected andprovided to the particular user. According to various embodiments, a setof weights and functions use a combination of rule based and fuzzy logicbased decision making to determine the areas of maximal overlap betweenthe priming repository system and the personalization repository system.Clustering analysis may be performed to determine clustering of primingbased preferences and personalization based preferences along a commonnormalized dimension, such as a subset or group of individuals. Inparticular embodiments, a set of weights and algorithms are used to mappreferences in the personalization repository to identified maxima forpriming.

According to various embodiments, the advertisement exchange includes adata analyzer associated with the data cleanser 121. The data analyzeruses a variety of mechanisms to analyze underlying data in the system todetermine resonance. According to various embodiments, the data analyzercustomizes and extracts the independent neurological andneuro-physiological parameters for each individual in each modality, andblends the estimates within a modality as well as across modalities toelicit an enhanced response to the presented stimulus material. Inparticular embodiments, the data analyzer aggregates the responsemeasures across subjects in a dataset.

According to various embodiments, neurological and neuro-physiologicalsignatures are measured using time domain analyses and frequency domainanalyses. Such analyses use parameters that are common acrossindividuals as well as parameters that are unique to each individual.The analyses could also include statistical parameter extraction andfuzzy logic based attribute estimation from both the time and frequencycomponents of the synthesized response.

In some examples, statistical parameters used in a blended effectivenessestimate include evaluations of skew, peaks, first and second moments,distribution, as well as fuzzy estimates of attention, emotionalengagement and memory retention responses.

According to various embodiments, the data analyzer may include anintra-modality response synthesizer and a cross-modality responsesynthesizer. In particular embodiments, the intra-modality responsesynthesizer is configured to customize and extract the independentneurological and neurophysiological parameters for each individual ineach modality and blend the estimates within a modality analytically toelicit an enhanced response to the presented stimuli. In particularembodiments, the intra-modality response synthesizer also aggregatesdata from different subjects in a dataset.

According to various embodiments, the cross-modality responsesynthesizer or fusion device blends different intra-modality responses,including raw signals and signals output. The combination of signalsenhances the measures of effectiveness within a modality. Thecross-modality response fusion device can also aggregate data fromdifferent subjects in a dataset.

According to various embodiments, the data analyzer also includes acomposite enhanced effectiveness estimator (CEEE) that combines theenhanced responses and estimates from each modality to provide a blendedestimate of the effectiveness. In particular embodiments, blendedestimates are provided for each exposure of a subject to stimulusmaterials. The blended estimates are evaluated over time to assessresonance characteristics. According to various embodiments, numericalvalues are assigned to each blended estimate. The numerical values maycorrespond to the intensity of neuro-response measurements, thesignificance of peaks, the change between peaks, etc. Higher numericalvalues may correspond to higher significance in neuro-responseintensity. Lower numerical values may correspond to lower significanceor even insignificant neuro-response activity. In other examples,multiple values are assigned to each blended estimate. In still otherexamples, blended estimates of neuro-response significance aregraphically represented to show changes after repeated exposure.

According to various embodiments, a data analyzer passes data to aresonance estimator that assesses and extracts resonance patterns. Inparticular embodiments, the resonance estimator determines entitypositions in various stimulus segments and matches position informationwith eye tracking paths while correlating saccades with neuralassessments of attention, memory retention, and emotional engagement. Inparticular embodiments, the resonance estimator stores data in thepriming repository system. As with a variety of the components in thesystem, various repositories can be co-located with the rest of thesystem and the user, or could be implemented in remote locations.

Data from various sources including survey based data 137 may be blendedand passed to an advertisement exchange 135. In some examples, surveybased data 137 and demographic data may be used without neuro-responsedata. According to various embodiments, the advertisement exchange 135manages advertisements such as commercials and print banners andidentifies slots having characteristics appropriate for theadvertisements. Appropriateness may be based on advertisement type,neuro-response characteristics of advertisements, neuro-responsecharacteristics of advertisement slots, demographic information, etc.Advertisement slots in a commercial pod may be offered to a variety ofadvertisers, companies, firms, and individuals. In some examples,advertisement slots may be auctioned using variety of bid mechanisms.Characteristics of a slot in a particular commercial pod may be modifiedas other slots in the pod are sold on a real-time basis. It isrecognized that the programming as well as other advertisementssurrounding an advertisement slot affect priming, attention, engagement,and retention characteristics of the advertisement slot.

Commercials in a pod may be ordered in a particular manner to optimizeeffectiveness. Advertisements on a page may be rearranged to improveviewer response. According to various embodiments, the advertisementexchange 135 receives bids, selects, and assembles in a real time, anear real time, or a time delayed manner advertisements for placement inadvertisement slots by associating neuro-response characteristics ofslots with characteristics of advertisements.

FIG. 2 illustrates examples of data models that may be used with anadvertisement exchange. According to various embodiments, a stimulusattributes data model 201 includes a channel 203, media type 205, timespan 207, audience 209, and demographic information 211. A stimuluspurpose data model 213 may include intents 215 and objectives 217.According to various embodiments, stimulus purpose data model 213 alsoincludes spatial and temporal information 219 about entities andemerging relationships between entities.

According to various embodiments, another stimulus attributes data model221 includes creation attributes 223, ownership attributes 225,broadcast attributes 227, and statistical, demographic and/or surveybased identifiers 229 for automatically integrating theneuro-physiological and neuro-behavioral response with other attributesand meta-information associated with the stimulus.

According to various embodiments, a stimulus priming data model 231includes fields for identifying advertisement breaks 233 and scenes 235that can be associated with various priming levels 237 and audienceresonance measurements 239. In particular embodiments, the data model231 provides temporal and spatial information for ads, scenes, events,locations, etc. that may be associated with priming levels and audienceresonance measurements. In some examples, priming levels for a varietyof products, services, offerings, etc. are correlated with temporal andspatial information in source material such as a movie, billboard,advertisement, commercial, store shelf, etc. In some examples, the datamodel associates with each second of a show a set of meta-tags forpre-break content indicating categories of products and services thatare primed. The level of priming associated with each category ofproduct or service at various insertions points may also be provided.Audience resonance measurements and maximal audience resonancemeasurements for various scenes and advertisement breaks may bemaintained and correlated with sets of products, services, offerings,etc.

The priming and resonance information may be used to selectadvertisements suited for particular levels of priming and resonancecorresponding to identified advertisement slots.

FIG. 3 illustrates examples of data models that can be used for storageof information associated with tracking and measurement of resonance.According to various embodiments, a dataset data model 301 includes anexperiment name 303 and/or identifier, client attributes 305, a subjectpool 307, logistics information 309 such as the location, date, and timeof testing, and stimulus material 311 including stimulus materialattributes.

In particular embodiments, a subject attribute data model 315 includes asubject name 317 and/or identifier, contact information 321, anddemographic attributes 319 that may be useful for review of neurologicaland neuro-physiological data. Some examples of pertinent demographicattributes include marriage status, employment status, occupation,household income, household size and composition, ethnicity, geographiclocation, sex, race. Other fields that may be included in data model 315include subject preferences 323 such as shopping preferences,entertainment preferences, and financial preferences. Shoppingpreferences include favorite stores, shopping frequency, categoriesshopped, favorite brands. Entertainment preferences includenetwork/cable/satellite access capabilities, favorite shows, favoritegenres, and favorite actors. Financial preferences include favoriteinsurance companies, preferred investment practices, bankingpreferences, and favorite online financial instruments. A variety ofproduct and service attributes and preferences may also be included. Avariety of subject attributes may be included in a subject attributesdata model 315 and data models may be preset or custom generated to suitparticular purposes.

According to various embodiments, data models for neuro-feedbackassociation 325 identify experimental protocols 327, modalities included329 such as EEG, EOG, FMRI, surveys conducted, and experiment designparameters 333 such as segments and segment attributes. Other fields mayinclude experiment presentation scripts, segment length, segment detailslike stimulus material used, inter-subject variations, intra-subjectvariations, instructions, presentation order, survey questions used,etc. Other data models may include a data collection data model 337.According to various embodiments, the data collection data model 337includes recording attributes 339 such as station and locationidentifiers, the data and time of recording, and operator details. Inparticular embodiments, equipment attributes 341 include an amplifieridentifier and a sensor identifier.

Modalities recorded 343 may include modality specific attributes likeEEG cap layout, active channels, sampling frequency, and filters used.EOG specific attributes include the number and type of sensors used,location of sensors applied, etc. Eye tracking specific attributesinclude the type of tracker used, data recording frequency, data beingrecorded, recording format, etc. According to various embodiments, datastorage attributes 345 include file storage conventions (format, namingconvention, dating convention), storage location, archival attributes,expiry attributes, etc.

A preset query data model 349 includes a query name 351 and/oridentifier, an accessed data collection 353 such as data segmentsinvolved (models, databases/cubes, tables, etc.), access securityattributes 355 included who has what type of access, and refreshattributes 357 such as the expiry of the query, refresh frequency, etc.Other fields such as push-pull preferences can also be included toidentify an auto push reporting driver or a user driven report retrievalsystem.

FIG. 4 illustrates examples of queries that can be performed to obtaindata associated with an advertisement exchange. According to variousembodiments, queries are defined from general or customized scriptinglanguages and constructs, visual mechanisms, a library of presetqueries, diagnostic querying including drill-down diagnostics, andeliciting what if scenarios. According to various embodiments, subjectattributes queries 415 may be configured to obtain data from aneuro-informatics repository using a location 417 or geographicinformation, session information 421 such as testing times and dates,and demographic attributes 419. Demographics attributes includehousehold income, household size and status, education level, age ofkids, etc.

Other queries may retrieve stimulus material based on shoppingpreferences of subject participants, countenance, physiologicalassessment, completion status. For example, a user may query for dataassociated with product categories, products shopped, shops frequented,subject eye correction status, color blindness, subject state, signalstrength of measured responses, alpha frequency band ringers, musclemovement assessments, segments completed, etc. Experimental design basedqueries 425 may obtain data from a neuro-informatics repository based onexperiment protocols 427, product category 429, surveys included 431,and stimulus provided 433. Other fields that may be used include thenumber of protocol repetitions used, combination of protocols used, andusage configuration of surveys.

Client and industry based queries may obtain data based on the types ofindustries included in testing, specific categories tested, clientcompanies involved, and brands being tested. Response assessment basedqueries 437 may include attention scores 439, emotion scores, 441,retention scores 443, and effectiveness scores 445. Such queries mayobtain materials that elicited particular scores.

Response measure profile based queries may use mean measure thresholds,variance measures, number of peaks detected, etc. Group response queriesmay include group statistics like mean, variance, kurtosis, p-value,etc., group size, and outlier assessment measures. Still other queriesmay involve testing attributes like test location, time period, testrepetition count, test station, and test operator fields. A variety oftypes and combinations of types of queries can be used to efficientlyextract data.

FIG. 5 illustrates examples of reports that can be generated. Accordingto various embodiments, client assessment summary reports 501 includeeffectiveness measures 503, component assessment measures 505, andresonance measures 507. Effectiveness assessment measures includecomposite assessment measure(s), industry/category/client specificplacement (percentile, ranking, etc.), actionable grouping assessmentsuch as removing material, modifying segments, or fine tuning specificelements, etc, and the evolution of the effectiveness profile over time.In particular embodiments, component assessment reports includecomponent assessment measures like attention, emotional scores,percentile placement, ranking, etc. Component profile measures includetime based evolution of the component measures and profile statisticalassessments. According to various embodiments, reports include thenumber of times material is assessed, attributes of the multiplepresentations used, evolution of the response assessment measures overthe multiple presentations, and usage recommendations.

According to various embodiments, client cumulative reports 511 includemedia grouped reporting 513 of all stimulus assessed, campaign groupedreporting 515 of stimulus assessed, and time/location grouped reporting517 of stimulus assessed. According to various embodiments, industrycumulative and syndicated reports 521 include aggregate assessmentresponses measures 523, top performer lists 525, bottom performer lists527, outliers 529, and trend reporting 531. In particular embodiments,tracking and reporting includes specific products, categories,companies, brands.

FIG. 6 illustrates one example of building a priming repository systemfor an advertisement exchange. Although priming characteristics aredescribed, it should be noted that other neuro-response characteristicssuch as retention, engagement, resonance, etc., may also be obtained. At601, stimulus material is provided to multiple subjects. According tovarious embodiments, stimulus includes streaming video and audio. Inparticular embodiments, subjects view stimulus in their own homes ingroup or individual settings. In some examples, verbal and writtenresponses are collected for use without neuro-response measurements. Inother examples, verbal and written responses are correlated withneuro-response measurements. At 603, subject neuro-response measurementsare collected using a variety of modalities, such as EEG, ERP, EOG,FMRI, etc. At 605, data is passed through a data cleanser to removenoise and artifacts that may make data more difficult to interpret.According to various embodiments, the data cleanser removes EEGelectrical activity associated with blinking and otherendogenous/exogenous artifacts.

According to various embodiments, data analysis is performed. Dataanalysis may include intra-modality response synthesis andcross-modality response synthesis to enhance effectiveness measures. Itshould be noted that in some particular instances, one type of synthesismay be performed without performing other types of synthesis. Forexample, cross-modality response synthesis may be performed with orwithout intra-modality response synthesis. In other examples,intra-modality response synthesis may be performed withoutcross-modality response synthesis.

A variety of mechanisms can be used to perform data analysis. Inparticular embodiments, a stimulus attributes repository is accessed toobtain attributes and characteristics of the stimulus materials, alongwith purposes, intents, objectives, etc. In particular embodiments, EEGresponse data is synthesized to provide an enhanced assessment ofeffectiveness. According to various embodiments, EEG measures electricalactivity resulting from thousands of simultaneous neural processesassociated with different portions of the brain. EEG data can beclassified in various bands. According to various embodiments, brainwavefrequencies include delta, theta, alpha, beta, and gamma frequencyranges. Delta waves are classified as those less than 4 Hz and areprominent during deep sleep. Theta waves have frequencies between 3.5 to7.5 Hz and are associated with memories, attention, emotions, andsensations. Theta waves are typically prominent during states ofinternal focus.

Alpha frequencies reside between 7.5 and 13 Hz and typically peak around10 Hz. Alpha waves are prominent during states of relaxation. Beta waveshave a frequency range between 14 and 30 Hz. Beta waves are prominentduring states of motor control, long range synchronization between brainareas, analytical problem solving, judgment, and decision making Gammawaves occur between 30 and 60 Hz and are involved in binding ofdifferent populations of neurons together into a network for the purposeof carrying out a certain cognitive or motor function, as well as inattention and memory. Because the skull and dermal layers attenuatewaves in this frequency range, brain waves above 75-80 Hz are difficultto detect and are often not used for stimuli response assessment.

However, the techniques and mechanisms of the present disclosurerecognize that analyzing high gamma band (kappa-band: Above 60 Hz)measurements, in addition to theta, alpha, beta, and low gamma bandmeasurements, enhances neurological attention, emotional engagement andretention component estimates. In particular embodiments, EEGmeasurements including difficult to detect high gamma or kappa bandmeasurements are obtained, enhanced, and evaluated. Subject and taskspecific signature sub-bands in the theta, alpha, beta, gamma and kappabands are identified to provide enhanced response estimates. Accordingto various embodiments, high gamma waves (kappa-band) above 80 Hz(typically detectable with sub-cranial EEG and/ormagnetoencephalography) can be used in inverse model-based enhancementof the frequency responses to the stimuli.

Various embodiments of the present disclosure recognize that particularsub-bands within each frequency range have particular prominence duringcertain activities. A subset of the frequencies in a particular band isreferred to herein as a sub-band. For example, a sub-band may includethe 40-45 Hz range within the gamma band. In particular embodiments,multiple sub-bands within the different bands are selected whileremaining frequencies are band pass filtered. In particular embodiments,multiple sub-band responses may be enhanced, while the remainingfrequency responses may be attenuated.

An information theory based band-weighting model is used for adaptiveextraction of selective dataset specific, subject specific, taskspecific bands to enhance the effectiveness measure. Adaptive extractionmay be performed using fuzzy scaling. Stimuli can be presented andenhanced measurements determined multiple times to determine thevariation profiles across multiple presentations. Determining variousprofiles provides an enhanced assessment of the primary responses aswell as the longevity (wear-out) of the marketing and entertainmentstimuli. The synchronous response of multiple individuals to stimulipresented in concert is measured to determine an enhanced across subjectsynchrony measure of effectiveness. According to various embodiments,the synchronous response may be determined for multiple subjectsresiding in separate locations or for multiple subjects residing in thesame location.

Although a variety of synthesis mechanisms are described, it should berecognized that any number of mechanisms can be applied—in sequence orin parallel with or without interaction between the mechanisms.

Although intra-modality synthesis mechanisms provide enhancedsignificance data, additional cross-modality synthesis mechanisms canalso be applied. A variety of mechanisms such as EEG, eye tracking,FMRI, EOG, and facial emotion encoding are connected to a cross-modalitysynthesis mechanism. Other mechanisms as well as variations andenhancements on existing mechanisms may also be included. According tovarious embodiments, data from a specific modality can be enhanced usingdata from one or more other modalities. In particular embodiments, EEGtypically makes frequency measurements in different bands like alpha,beta and gamma to provide estimates of significance. However, thetechniques of the present disclosure recognize that significancemeasures can be enhanced further using information from othermodalities.

For example, facial emotion encoding measures can be used to enhance thevalence of the EEG emotional engagement measure. EOG and eye trackingsaccadic measures of object entities can be used to enhance the EEGestimates of significance including but not limited to attention,emotional engagement, and memory retention. According to variousembodiments, a cross-modality synthesis mechanism performs time andphase shifting of data to allow data from different modalities to align.In some examples, it is recognized that an EEG response will often occurhundreds of milliseconds before a facial emotion measurement changes.Correlations can be drawn and time and phase shifts made on anindividual as well as a group basis. In other examples, saccadic eyemovements may be determined as occurring before and after particular EEGresponses. According to various embodiments, time corrected FMRImeasures are used to scale and enhance the EEG estimates of significanceincluding attention, emotional engagement and memory retention measures.

Evidence of the occurrence or non-occurrence of specific time domaindifference event-related potential components (like the DERP) inspecific regions correlates with subject responsiveness to specificstimulus. According to various embodiments, ERP measures are enhancedusing EEG time-frequency measures (ERPSP) in response to thepresentation of the marketing and entertainment stimuli. Specificportions are extracted and isolated to identify ERP, DERP and ERPSPanalyses to perform. In particular embodiments, an EEG frequencyestimation of attention, emotion and memory retention (ERPSP) is used asa co-factor in enhancing the ERP, DERP and time-domain responseanalysis.

EOG measures saccades to determine the presence of attention to specificobjects of stimulus. Eye tracking measures the subject's gaze path,location and dwell on specific objects of stimulus. According to variousembodiments, EOG and eye tracking is enhanced by measuring the presenceof lambda waves (a neurophysiological index of saccade effectiveness) inthe ongoing EEG in the occipital and extra striate regions, triggered bythe slope of saccade-onset to estimate the significance of the EOG andeye tracking measures. In particular embodiments, specific EEGsignatures of activity such as slow potential shifts and measures ofcoherence in time-frequency responses at the Frontal Eye Field (FEF)regions that preceded saccade-onset are measured to enhance theeffectiveness of the saccadic activity data.

According to various embodiments, facial emotion encoding uses templatesgenerated by measuring facial muscle positions and movements ofindividuals expressing various emotions prior to the testing session.These individual specific facial emotion encoding templates are matchedwith the individual responses to identify subject emotional response. Inparticular embodiments, these facial emotion encoding measurements areenhanced by evaluating inter-hemispherical asymmetries in EEG responsesin specific frequency bands and measuring frequency band interactions.The techniques of the present disclosure recognize that not only areparticular frequency bands significant in EEG responses, but particularfrequency bands used for communication between particular areas of thebrain are significant. Consequently, these EEG responses enhance theEMG, graphic and video based facial emotion identification.

According to various embodiments, post-stimulus versus pre-stimulusdifferential measurements of ERP time domain components in multipleregions of the brain (DERP) are measured at 607. The differentialmeasures give a mechanism for eliciting responses attributable to thestimulus. For example the messaging response attributable to anadvertisement or the brand response attributable to multiple brands isdetermined using pre-resonance and post-resonance estimates

At 609, target versus distracter stimulus differential responses aredetermined for different regions of the brain (DERP). At 611, eventrelated time-frequency analysis of the differential response (DERPSPs)are used to assess the attention, emotion and memory retention measuresacross multiple frequency bands. According to various embodiments, themultiple frequency bands include theta, alpha, beta, gamma and highgamma or kappa. At 613, priming levels and resonance for variousproducts, services, and offerings are determined at different locationsin the stimulus material. In some examples, priming levels and resonanceare manually determined. In other examples, priming levels and resonanceare automatically determined using neuro-response measurements.According to various embodiments, video streams are modified withdifferent inserted advertisements for various products and services todetermine the effectiveness of the inserted advertisements based onpriming levels and resonance of the source material.

At 617, multiple trials are performed to enhance priming and resonancemeasures. In particular embodiments, the priming and resonance measuresare sent to a priming repository 619. The priming repository 619 may beused to automatically select and place advertising suited for particularslots in a cluster. Advertisements may be automatically selected andarranged in advertisement slots to increase effectiveness.

FIG. 7 illustrates an example of a technique for implementing anadvertisement exchange. At 701, stimulus material characteristics arereceived. Stimulus material characteristics may include type of media,length, duration, demographic, ratings, etc. At 703, advertisement slotsin the stimulus material are received. According to various embodiments,advertisement slots may not only include commercial slots in acommercial pod, but may also include blank billboards in a televisionprogram that are suitable for insertion of a display advertisement.Advertisement slots may also include locations of banner advertisementsthat may be placed on a page, or locations of images that may be placedon store shelves. Advertisement slots may be received as identifiersthat include information such as duration, time, location, etc.

Any temporal or spatial location suitable for advertising may beidentified at 703. According to various embodiments, neuro-response datais obtained for the various advertisement slots at 705. Obtainingneuro-response data may involve evaluating stimulus material todetermine attention, engagement, retention, and priming characteristicsfor various temporal and spatial locations in the stimulus material. Theattention, engagement, retention, and priming characteristics may beevaluated for various types of products, services, offers, goods, etc.In some examples, only priming characteristics are determined forvarious advertisement slots. In other examples, a variety ofneuro-response characteristics including mirroring characteristics,propensity to act, propensity to reach, emotional levels, etc., may bedetermined.

According to various embodiments, advertisement slot characteristics areassociated with advertisement slots at 707. Advertisement slotcharacteristics may include not only neuro-response characteristics butmay also include demographic information, media type, duration, length,cost, audience exposure, etc. At 709, advertisement slots are providedto advertisers and advertisement slot offers are received. In someexamples, advertisement slot offers are received based on an auction,reverse auction, Dutch auction, or anonymous bid process. In otherexamples, advertisement slots are sold on a tiered scale based onadvertisement characteristics and perceived desirability to advertisers.

At 711, advertisements are matched with advertisement slots. At 713,advertisement slot characteristics may be updated for surrounding slotsbased on sold advertisements. It is recognized that an advertisement mayaffect priming, engagement, and other characteristics for otheradvertisement slots. According to various embodiments, advertisementslot characteristics are dynamically adjusted as advertisements aresold.

According to various embodiments, various mechanisms such as the datacollection mechanisms, the intra-modality synthesis mechanisms,cross-modality synthesis mechanisms, etc. are implemented on multipledevices. However, it is also possible that the various mechanisms beimplemented in hardware, firmware, and/or software in a single system.FIG. 8 provides one example of a system that can be used to implementone or more mechanisms. For example, the system shown in FIG. 8 may beused to implement a resonance measurement system.

According to particular example embodiments, a system 800 suitable forimplementing particular embodiments of the present disclosure includes aprocessor 801, a memory 803, an interface 811, and a bus 815 (e.g., aPCI bus). When acting under the control of appropriate software orfirmware, the processor 801 is responsible for tasks such as patterngeneration. Various specially configured devices can also be used inplace of a processor 801 or in addition to processor 801. The completeimplementation can also be done in custom hardware. The interface 811 istypically configured to send and receive data packets or data segmentsover a network. Particular examples of interfaces the device supportsinclude host bus adapter (HBA) interfaces, Ethernet interfaces, framerelay interfaces, cable interfaces, DSL interfaces, token ringinterfaces, and the like.

According to particular example embodiments, the system 800 uses memory803 to store data, algorithms and program instructions. The programinstructions may control the operation of an operating system and/or oneor more applications, for example. The memory or memories may also beconfigured to store received data and process received data.

Because such information and program instructions may be employed toimplement the systems/methods described herein, the present disclosurerelates to tangible, machine readable media that include programinstructions, state information, etc. for performing various operationsdescribed herein. Examples of machine-readable media include, but arenot limited to, magnetic media such as hard disks, floppy disks, andmagnetic tape; optical media such as CD-ROM disks and DVDs;magneto-optical media such as optical disks; and hardware devices thatare specially configured to store and perform program instructions, suchas read-only memory devices (ROM) and random access memory (RAM).Examples of program instructions include both machine code, such asproduced by a compiler, and files containing higher level code that maybe executed by the computer using an interpreter.

Although the foregoing disclosure has been described in some detail forpurposes of clarity of understanding, it will be apparent that certainchanges and modifications may be practiced within the scope of theappended claims. Therefore, the present embodiments are to be consideredas illustrative and not restrictive and the disclosure is not to belimited to the details given herein, but may be modified within thescope and equivalents of the appended claims.

The status of the claims:
 1. A method, comprising: determiningadvertisement slot characteristics for a plurality of advertisementslots, the advertisement slot characteristics including subjectresonance measured by: determining a first event related potential fromneuro-response data gathered from multiple regions of a brain of asubject; determining a second event related potential from theneuro-response data; and calculating a differential measurement of thefirst event related potential and the second event related potential,the subject resonance based on the differential measurement; andmatching the plurality of advertisement slots with a plurality ofadvertisements based on the advertisement slot characteristics.
 2. Themethod of claim 1, wherein the neuro-response data is representative ofone or more of retention, engagement, attention, or a priming level. 3.The method of claim 1, wherein the advertisement slots are commercialsin a commercial pod.
 4. A method, comprising: determining advertisementslot characteristics for a plurality of advertisement slots associatedwith media materials, the advertisement slot characteristics determinedby: determining a first event related potential from neuro-response datagathered from multiple regions of a brain of a subject; determining asecond event related potential from the neuro-response data; andcalculating a differential measurement of the first event relatedpotential and the second event related potential, the subject resonancebased on the differential measurement; matching a first advertisementslot with a first advertisement based on at least one of theadvertisement slot characteristics; and revising one or more of theadvertisement slot characteristics for the plurality of remainingadvertisement slots based on the first advertisement.
 5. The method ofclaim 4, wherein the advertisement slot characteristics comprise primingcharacteristics. 6-20. (canceled)
 21. A system, comprising: aneuro-response data evaluator to determine advertisement slotcharacteristics for a plurality of advertisement slots, theadvertisement slot characteristics including subject resonance measuredby: determining a first event related potential from neuro-response datagathered from multiple regions of a brain of a subject; determining asecond event related potential from the neuro-response data; andcalculating a differential measurement of the first event relatedpotential and the second event related potential, the subject resonancebased on the differential measurement; and a processor to match theplurality of advertisement slots with a plurality of advertisementsbased on the advertisement slot characteristics.
 22. The system of claim21, wherein the neuro-response data is representative of one or more ofretention, engagement, attention, or a priming level.
 23. The system ofclaim 21, wherein the advertisement slots are commercials in acommercial pod.
 24. A system, comprising: a processor to: determineadvertisement slot characteristics for a plurality of advertisementslots associated with media materials, the advertisement slotcharacteristics determined by: determining a first event relatedpotential from neuro-response data gathered from multiple regions of abrain of a subject; determining a second event related potential fromthe neuro-response data; and calculating a differential measurement ofthe first event related potential and the second event relatedpotential, the subject resonance based on the differential measurement;match a first advertisement slot with a first advertisement based on theadvertisement slot characteristics; and revise one or more of theadvertisement slot characteristics for the plurality of remainingadvertisement slots based on the first advertisement.
 25. The system ofclaim 22, wherein the advertisement slot characteristics comprisepriming characteristics. 26-40. (canceled)
 41. A tangible machinereadable storage medium comprising instructions, which when executed,cause a machine to at least: determine advertisement slotcharacteristics for a plurality of advertisement slots, theadvertisement slot characteristics including subject resonance measuredby: determining a first event related potential from neuro-response datagathered from multiple regions of a brain of a subject; determining asecond event related potential from the neuro-response data; andcalculating a differential measurement of the first event relatedpotential and the second event related potential, the subject resonancebased on the differential measurement; and match the plurality ofadvertisement slots with a plurality of advertisements based on theadvertisement slot characteristics.
 42. The medium of claim 41, whereinthe neuro-response data is representative of one or more of retention,engagement, attention, or a priming level.
 43. The medium of claim 41,wherein the advertisement slots are commercials in a commercial pod. 44.A tangible machine readable storage medium comprising instructions,which when executed, cause a machine to at least: determineadvertisement slot characteristics for a plurality of advertisementslots associated with media materials, the advertisement slotcharacteristics determined by: determining a first event relatedpotential from neuro-response data gathered from multiple regions of abrain of a subject; determining a second event related potential fromthe neuro-response data; and calculating a differential measurement ofthe first event related potential and the second event relatedpotential, the subject resonance based on the differential measurement;match a first advertisement slot with a first advertisement based on theadvertisement slot characteristics; and revise one or more of theadvertisement slot characteristics for the plurality of remainingadvertisement slots based on the first advertisement.
 45. The medium ofclaim 44, wherein the advertisement slot characteristics comprisepriming characteristics. 46-96. (canceled)